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<p>Causal Inference is a process to determine how the changes in the treatment affect the outcome.</p>
<p><strong>Learning Objective</strong>:</p>
<ul>
<li>Introduction to causality and various related terms</li>
<li>Assumptions necessary for proving causality</li>
<li>Confounding and the ways of handling it</li>
<li>Modeling approaches for proving causal inference and the steps involved</li>
<li>The approach of Inverse Probability Treatment Weighting (IPTW) and its application in Marginal Structural Model (MSM)</li>
<li>An example with MIMIC-III</li>
<li>Closing Summary and Next Steps for Independent Learning</li>
</ul>
</div>
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<h2 class="hd hd-2 unit-title">Notations</h2>
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<h2 class="hd hd-2 unit-title">Introduction to Causal Inference</h2>
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<p><strong>Learning Objectives:</strong></p>
<ul>
<li>Introduction to the causal inference terminology</li>
<li>Assumptions for this module</li>
<li>Study designs</li>
<li>Potential outcomes and the counterfactuals</li>
<li>Causality as a hypothetical intervention</li>
</ul>
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<div class="wrapper-problem-response" tabindex="-1" aria-label="Question 1" role="group"><p>The causal effects are difficult to define for _____</p>
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Multiple Choice
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<div class="wrapper-problem-response" tabindex="-1" aria-label="Question 1" role="group"><p>What is the formula for average causal effect of the treatment on the treated?</p>
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<p><a href="/assets/courseware/v1/bed8b76ec9bed29bc00d70619fb2b73c/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Introduction_to_Causal_Inference.pptx" target="[object Object]">Introduction to Causal Inference.pptx</a></p>
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<h2 class="hd hd-2 unit-title">Identifiability</h2>
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<h3 class="hd hd-2">Identifiability</h3>
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<p><strong>Learning Objectives:</strong></p>
<ol>
<li>SUTVA</li>
<ul>
<li>No Interference </li>
<li>One Version of Treatment </li>
</ul>
<li>Consistency Assumption </li>
<li>Ignorability / Exchangeability Assumption </li>
<li>Positivity Assumption </li>
<li>Stratification and Example</li>
</ol>
</div>
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Short Quiz
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<p>1. Select the correct equation for consistency assumption </p>
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<p>2. Select True or False: As per one of the assumption of Causal Inference, a treatment can have multiple versions</p>
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<div class="wrapper-problem-response" tabindex="-1" aria-label="Question 1" role="group"><p>3. Another name for Positivity assumption is</p>
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<p><a href="/assets/courseware/v1/ad6930b249f97ae52a3affc8794a6263/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Slide_3_.pptx" target="[object Object]">Identifiability.pptx</a></p>
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<h2 class="hd hd-2 unit-title">Confounding</h2>
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<h3 class="hd hd-2">Confounding</h3>
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<p><strong>Learning Objectives</strong>:</p>
<ul>
<li>Confounding</li>
<li>Causal Graphs</li>
<li>Backdoor Paths</li>
<li>Randomized Control Trials</li>
<li>Conditional Exchangeability</li>
<li>Blocking the Backdoor Path</li>
<li>Conditional Vs Marginal Effect</li>
</ul>
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<div class="wrapper-problem-response" tabindex="-1" aria-label="Question 1" role="group"><p>A confounder is a covariate that is: </p>
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<input type="radio" name="input_ed22b3f0fed0401c9930fe94b8db70fa_2_1" id="input_ed22b3f0fed0401c9930fe94b8db70fa_2_1_choice_2" class="field-input input-radio" value="choice_2"/><label id="ed22b3f0fed0401c9930fe94b8db70fa_2_1-choice_2-label" for="input_ed22b3f0fed0401c9930fe94b8db70fa_2_1_choice_2" class="response-label field-label label-inline" aria-describedby="status_ed22b3f0fed0401c9930fe94b8db70fa_2_1"> a mediator of the exposure-outcome relationship
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<h2 class="hd hd-2 unit-title">Modeling Approaches</h2>
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<ol></ol>
<p><strong>Learning Objectives:</strong></p>
<ol>
<li><span style="font-size: 1em;">Observational Studies and its types </span></li>
<li><span style="font-size: 1em;">Matching and its types</span></li>
<li><span style="font-size: 1em;">Assessing balance</span></li>
<li><span style="font-size: 1em;">Sensitivity Analysis</span></li>
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<p>1. Select true or false, in cohort studies the participant must not be free of outcome at the beginning of follow up </p>
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<p><strong>Learning Objectives</strong>:</p>
<ul>
<li>Inverse Probability Treatment Weighting (IPTW)</li>
<li>Marginal Structural Modeling (MSM)</li>
<li>IPTW estimation in MSM</li>
<li>Assessing balance</li>
<li>Distribution of weights and large weights</li>
<li>Doubly-Robust Estimators</li>
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<p><strong>Learning Objective</strong>: To understand the program that evaluates the causal risk in R on MIMIC-III dataset. The program evaluates the causal risk of using Indwelling Arterial Catheter (IAC) on the mortality of hemodynamically stable patients with a respiratory disorder.</p>
<p><strong>Note</strong>: In the video, based on the R program and the sample dataset, the risk difference is low, which means that there is a small benefit in using IAC.</p>
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<ol>
<li>Summary of what we have covered in this module </li>
<li>Things to try on your own </li>
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<li>Effect Modification </li>
<li>Time Varying Confounding </li>
<li>Instrumental Variables</li>
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<p><a href="/assets/courseware/v1/560e27c86e3a85b643f46c5424752201/asset-v1:MITx+HST.953x+3T2020+type@asset+block/Slide_8.pptx" target="[object Object]">Summary.pptx</a></p>
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